Analysis of Music Transition in Acoustic Feature Space for Music Recommendation

Previously, we proposed a playlist recommendation method that recommends a music sequence that has smooth transitions of the acoustic features in the two-dimensional music feature space. Our previous method recommends users using the last two songs in the playlist for the next songs that have a smooth transition of acoustic features from the current songs. Experimental results showed the usefulness of our proposed method comparing with baseline methods. However, the evaluation of whether or not the recommended song was truly a song suitable for the user has not been sufficient. In this paper, we analyze what kind of song sequence users feel smooth in music transition. We conduct a subjective experiment by nine subjects in their 20's, using a music data set composed of music data of 909 songs. The result shows the position to the music transition that users feel smooth.